Information Retrieval: Algorithms and Heuristics (The Springer International Series in Engineering and Computer Science) Review

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(More customer reviews)If you're working in the IR industry, or want to develop software in this field, this book is a great starting point. A clarification: this will is not a book for researchers -- instead think of it as a book for advanced practitioners or engineers needing to work in this area. Inside you'll see complete worked examples of several fundamental computations rather than detailed proofs.

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Information Retrieval: Algorithms and Heuristics is acomprehensive introduction to the study of information retrievalcovering both effectiveness and run-time performance. The focus of thepresentation is on algorithms and heuristics used to find documentsrelevant to the user request and to find them fast. Through multipleexamples, the most commonly used algorithms and heuristics needed aretackled. To facilitate understanding and applications, introductionsto and discussions of computational linguistics, natural languageprocessing, probability theory and library and computer science areprovided. While this text focuses on algorithms and not on commercialproduct per se, the basic strategies used by many commercial productsare described. Techniques that can be used to find information on theWeb, as well as in other large information collections, are included.This volume is an invaluable resource for researchers, practitioners,and students working in information retrieval and databases. Forinstructors, a set of Powerpoint slides, including speaker notes, areavailable online from the authors.

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Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics) Review

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(More customer reviews)A stunning 1000 page encyclopedia of spatial, multidimensional, and metric data structures and algorithms presented in the Knuth tradition. The general coverage is broader than an older, now out of print and expensive: "Design and Analysis of Spatial Data Structures". In a surprise, the new book is not only the size of a telephone directory, but it has double the number of useful pages. 4 extensive chapters cover data structures and algorithms for: points, objects and images, intervals and small rectangles, and the same data types in higher +dimensions. Within each chapter, the algorithms and clearly presented and are accompanied by an extensive use of figures. The algorithms which run from the expected to the exotic are summarized by the table of contents at the publisher's web site. Unexpected algorithms are also covered including: nearest neighbor finding which is useful for clustering applications, image pyramids, and object pyramids or hierarchies such as R-trees.The book has a textbook flavor with exercises at the end of each section where specifics are left for the student; however, solutions and pseudo-code for many of the exercises are in a 300+ page appendix maintaining the book as a useful reference. This book is comprehensive, inexpensive, and in my mind - a must have.

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Social Network Data Analytics Review

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(More customer reviews)This is a very interesting book for both researchers and practitioners in computer science who work in the area of data mining and want to learn the state-of-the-art in social network data analytics. The book provides good coverage of the subject area by focusing on popular research topics, such as the study of the statistical properties that are apparent in "typical" social networks, the problems of community detection and social influence analysis, the expert-location discovery problem, the privacy issues that arise in the context of social networks, as well as visualization techniques, text mining techniques and social tagging. The emerging area of integrating sensors and social networks is also examined. Each chapter of the book contains numerous bibliographic references that will guide readers who are interested in particular topics to explore these topics in more depth. Overall, I highly recommend this book!

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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.

Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.

This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

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